Five Things AI Has Changed In 2023
2023 will be remembered as the year that made AI mainstream. Starting at the end of 2022, we saw the release of ChatGPT, closely followed by Microsoft Bing AI, Google's Bard, and all the other AI tools released over the past ten months.
You should check out our other posts, where we share some interesting AI sites:
2023, The Year AI Said Hello World!
A lot can be written about this topic, and AI would probably agree that it's just getting started.
We got to see all the cool things that AI has made possible, from project management, writing emails, designing websites, writing code, creating videos and images from a prompt and everyone's favorite, using ChatGPT to answer queries on SQL syntax, generating SQL queries, or explain the output of a given SQL query.
All right, the last one is probably not your favorite, but it ranks right up there with "How can I improve my business?"
Five Things Forever Changed By AI
It's hard to pick five, as the impact of AI has been felt across multiple industries and deeply affected various aspects of our daily lives and society as a whole.
AI has revolutionized how businesses operate, healthcare is delivered, education is personalized, transportation is becoming autonomous, and how we interact with technology.
Its influence is not limited to a specific sector but permeates virtually every facet of modern life.
The content creation market was the first area with an immediate impact. Web-based AI services offered tools that would fix your writing, completely write everything for you, create images, create videos, make presentations and more, with sometimes nothing more than a prompt.
Whether you work in finance, healthcare or marketing, you are probably using AI to help you make your daily tasks easier.
These tools help you automate finance and accounting tasks like preparing invoices, labeling expenses, and preparing daily financial reports. In healthcare, AI can analyze patient data, such as medical images and lab results, to assist healthcare providers with diagnosis and treatment recommendations.
On the marketing side, AI can help you today with lead management, email marketing, and even help you manage your social media profiles.
The main aim of these tools is to improve efficiency and productivity, which is working as more such tools are constantly being introduced.
The list of sectors that use AI for automation is as wide as you can imagine and not just relevant to the three mentioned above.
Predictive analytics is the process of using data, statistical methods, and machine learning to forecast future outcomes and trends. AI can use machine learning algorithms to comb through historical data and make predictions based on identified patterns and trends.
Building forecasts, analyzing risks, optimizing business processes, statistical modeling, and data analysis are just a few areas in which AI can help to improve accuracy, efficiency and speed.
Since predictive analytics involves large amounts of data, having AI manage this process optimizes operations and helps businesses make informed decisions.
Even though this is technically not new, applications of some AI have been used by the defence sector in the past decade or so.
In recent years, we've seen further advancements in AI-driven autonomous vehicles, with some companies deploying limited commercial services for ride-sharing and delivery, like Get Cruise.
Commercial car companies like Tesla have also introduced advanced driver-assistance systems with AI capabilities.
While this is technically not a product or service, it's a very important 'Thing.'
With all the data fed to large language models (LLMs), there is a debate about how AI learns not to be biased and who is teaching it.
AI ethics are a set of guiding principles that stakeholders use to ensure that artificial intelligence technology is developed and used responsibly.
The most significant impact would be in the areas of:
- Privacy: Ethical considerations around how data is collected, stored, and used and giving individuals control over their personal information.
- Safety: Developing robust safety mechanisms, conducting thorough testing, and addressing risks associated with AI failures, especially in fields like autonomous vehicles and healthcare.
- Bias and Fairness: AI systems can amplify societal biases from training data, leading to unfair or discriminatory outcomes. Stakeholders should ensure fairness in AI by addressing bias and promoting fair decision-making.
We can't wait to see how AI evolves in 2024 and beyond.
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